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Applying Learnable Evolution Model to Heat Exchanger Design

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dc.contributor.author Kaufman, Kenneth A. en_US
dc.contributor.author Michalski, Ryszard S. en_US
dc.date.accessioned 2006-11-03T18:17:07Z
dc.date.available 2006-11-03T18:17:07Z
dc.date.issued 2000 en_US
dc.identifier.citation Kaufman, K. and Michalski, R. S., "Applying Learnable Evolution Model to Heat Exchanger Design," Proceedings of the Seventeenth National Conference on Artificial Intelligence (AAAI-2000) and Twelfth Annual Conference on Innovative Applications of Artificial Intelligence (IAAI-2000), Austin, TX, pp. 1014-1019, 2000. en_US
dc.identifier.uri http://hdl.handle.net/1920/1468
dc.description This article copyright © 2000 by the en_US
dc.description.abstract A new approach to evolutionary computation, called Learnable Evolution Model (LEM), has been applied to the problem of optimizing tube structures of heat exchangers. In contrast to conventional Darwinian-type evolutionary computation algorithms that use various forms of mutation and/or recombination operators, LEM employs machine learning to guide the process of generating new individuals. A system, ISHED1, based on LEM, automatically searches for the highest capacity heat exchangers under given technical and environmental constraints. The results of experiments have been highly promising, often producing solutions exceeding the best human designs. en_US
dc.format.extent 1844 bytes
dc.format.extent 393271 bytes
dc.format.extent 146984 bytes
dc.format.mimetype text/xml
dc.format.mimetype application/postscript
dc.format.mimetype application/pdf
dc.language.iso en_US en_US
dc.relation.ispartofseries P 00-10 en_US
dc.title Applying Learnable Evolution Model to Heat Exchanger Design en_US
dc.type Article en_US


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